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Be-CoDiS: a mathematical model to predict the risk of human diseases spread between countries – validation and application to the 2014–2015 ebola virus disease epidemic. (English) Zbl 1339.92085

Summary: Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and isolated cases in the UK, the USA and Spain. Regarding the emergency of this situation, there is a need for the development of decision tools, such as mathematical models, to assist the authorities to focus their efforts in important factors to eradicate Ebola. In this work, we propose a novel deterministic spatial-temporal model, called Between-Countries Disease Spread (Be-CoDiS), to study the evolution of human diseases within and between countries. The main interesting characteristics of Be-CoDiS are the consideration of the movement of people between countries, the control measure effects and the use of time-dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model and explain how its parameters and inputs are obtained. Then, in order to validate our approach, we consider two numerical experiments regarding the 2014-2015 Ebola epidemic. The first one studies the ability of the model in predicting the EVD evolution between countries starting from the index cases in Guinea in December 2013. The second one consists of forecasting the evolution of the epidemic by using some recent data. The results obtained with Be-CoDiS are compared to real data and other model outputs found in the literature. Finally, a brief parameter sensitivity analysis is done. A free MATLAB version of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm.

MSC:

92D30 Epidemiology

Software:

Be-CoDiS; Matlab

References:

[1] Anderson M (1979) Population biology of infectious diseases: part 1. Nature 280:361-367 · doi:10.1038/280361a0
[2] Astacio J, Briere D, Martinez J, F Rodriguez, Valenzuela-Campos N (2014) Mathematical models to study the outbreaks of ebola. Mathematical and theoretical biology report, Arizona State University
[3] Bowman C, Gumel A, Van den Driessche P, Wu J, Zhu H (2005) A mathematical model for assessing control strategies against West Nile virus. Bull Math Biol 67(5):1107-1133 · Zbl 1334.92392 · doi:10.1016/j.bulm.2005.01.002
[4] Brauer F, Castillo-Chávez C (2001) Mathematical models in population biology and epidemiology. Texts in applied mathematics. Springer, Berlin · Zbl 0967.92015 · doi:10.1007/978-1-4757-3516-1
[5] CDC (2014) Ebola disease. Centers for Disease Control. http://www.cdc.gov/vhf/ebola/
[6] Chowell G, Nishiura H (2014) Transmission dynamics and control of Ebola virus disease (EVD): a review. BMC Med 12:196 · doi:10.1186/s12916-014-0196-0
[7] Chowell G, Hengartner N, Castillo-Chavez C, Fenimore P, Hyman J (2004) The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. J Theor Biol 229(1):119-126 · Zbl 1440.92062 · doi:10.1016/j.jtbi.2004.03.006
[8] DeAngelis D, Gross L (1992) Individual-based models and approaches in ecology: populations, communities, and ecosystems. Chapman & Hall, London · doi:10.1007/978-1-4757-0869-1
[9] Diekmann O, Heesterbeek H, Britton T (2012) Mathematical tools for understanding infectious disease dynamics: Princeton series in theoretical and computational biology. Princeton University Press. http://www.jstor.org/stable/j.cttq9530 · Zbl 1304.92009
[10] Emond R, Evans B, Bowen E, Lloyd G (1977) A case of Ebola virus infection. Br Med J 2:541-544 · doi:10.1136/bmj.2.6086.541
[11] Fisman D, Khoo E, Tuite A (2014) Early epidemic dynamics of the West African 2014 Ebola outbreak: estimates derived with a simple two-parameter model. PLoS. doi:10.1371/currents.outbreaks.89c0d3783f36958d96ebbae97348d571 · Zbl 1315.76047
[12] Fonkwo P (2008) Pricing infectious disease. The economic and health implications of infectious diseases. EMBO Rep 9(1):S13-S17 · doi:10.1038/embor.2008.110
[13] Forgoston E, Schwartz I (2013) Predicting unobserved exposures from seasonal epidemic data. Bull Math Biol 75(9):1450-1471 · Zbl 1272.92048 · doi:10.1007/s11538-013-9855-0
[14] Gomes M, Pastore y Piontti A, Rossi L, Chao D, Longini I, Halloran M, Vespignani A (2014) Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLoS. doi:10.1371/currents.outbreaks.cd818f63d40e24aef769dda7df9e0da5 · Zbl 1315.76047
[15] Guy J, Nikola S (2014) Quantifying global international migration flows. Science 343(6178):1520-1522 · doi:10.1126/science.1248676
[16] Hernandez-Ceron N, Feng Z, Castillo-Chavez C (2013) Discrete epidemic models with arbitrary stage distributions and applications to disease control. Bull Math Biol 75(10):1716-1746 · Zbl 1310.92052 · doi:10.1007/s11538-013-9866-x
[17] Hewlett B, Hewlett B (2007) Ebola, culture and politics: The anthropology of an emerging disease. Case studies on contemporary social issues. Cengage Learning, Hampshire · Zbl 1334.92392
[18] Ivorra B, Redondo JL, Santiago JG, Ortigosa PM, Ramos AM (2013) Two- and three-dimensional modeling and optimization applied to the design of a fast hydrodynamic focusing microfluidic mixer for protein folding. Phys Fluids 25(3):032001 · Zbl 1315.76047 · doi:10.1063/1.4793612
[19] Ivorra B, Martínez-López B, Sánchez-Vizcaíno J, Ramos A (2014) Mathematical formulation and validation of the Be-FAST model for classical swine fever virus spread between and within farms. Ann Oper Res 219(1):25-47 · doi:10.1007/s10479-012-1257-4
[20] Legrand J, Grais R, Boelle P, Valleron A, Flahault A (2007) Understanding the dynamics of Ebola epidemics. Med Hypotheses 135(4):610-621
[21] Lekone P, Finkenstädt B (2006) Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics 62(4):1170-1177 · Zbl 1114.62120 · doi:10.1111/j.1541-0420.2006.00609.x
[22] Leroy E, Rouquet P, Formenty P, Souquière S, Kilbourne A, Froment J, Bermejo M, Smit S, Karesh W, Swanepoel R, Zaki S, Rollin P (2004) Multiple Ebola virus transmission events and rapid decline of central African wildlife. Science 303(5656):387-390 · doi:10.1126/science.1092528
[23] Martínez-López B, Ivorra B, Ramos A, Sánchez-Vizcaíno J (2011) A novel spatial and stochastic model to evaluate the within- and between-farm transmission of classical swine fever virus. I. General concepts and description of the model. Vet Microbiol 147(3-4):300-309 · doi:10.1016/j.vetmic.2010.07.009
[24] Martínez-López B, Ivorra B, Ngom D, Ramos A, Sánchez-Vizcaíno J (2012) A novel spatial and stochastic model to evaluate the within and between farm transmission of classical swine fever virus. II. Validation of the model. Vet Microbiol 155(1):21-32 · doi:10.1016/j.vetmic.2011.08.008
[25] Martínez-López B, Ivorra B, Ramos A, Fernández-Carrión E, Alexandrov T, Sánchez-Vizcaíno JM (2013) Evaluation of the risk of classical swine fever spread from backyard pigs to other domestic pigs by using the spatial stochastic disease spread model Be-FAST: The example of Bulgaria. Vet Microbiol 165(1-2):79-85 · doi:10.1016/j.vetmic.2013.01.045
[26] Martínez-López B, Ivorra B, Fernández-Carrión E, Perez A, Medel-Herrero A, Sánchez-Vizcaíno F, Gortázar C, Ramos A, Sánchez-Vizcaíno J (2014) A multi-analysis approach for space-time and economic evaluation of risks related with livestock diseases: The example of FMD in Peru. Prev Vet Med 114(1):47-63 · doi:10.1016/j.prevetmed.2014.01.013
[27] Massad E, Burattini M, Lopez L, Coutinho FA (2005) Forecasting versus projection models in epidemiology: the case of the SARS epidemics. Med Hypotheses 65(1):17-22 · doi:10.1016/j.mehy.2004.09.029
[28] Meltzer M, Atkins C, Santibanez S, Knust B, Petersen B, Ervin E, Nichol S, Damon I, Washington M (2014) Estimating the future number of cases in the Ebola epidemic—Liberia and Sierra Leone, 2014-2015. Centers for Disease Control MMWR/Early Release 63
[29] ONS (2013a) Examining the differences between the mid-year short-term immigration estimates and the 2011 Census for England and Wales. Office for National Statistics. http://www.ons.gov.uk/ons/dcp171776_312055
[30] ONS (2013b) Immigration patterns of non-UK born populations in England and Wales in 2011. Office for National Statistics. http://www.ons.gov.uk/ons/dcp171776_346219
[31] Peters CJ, Peters JW (1999) An introduction to Ebola: the virus and the disease. J Infect Dis 179(Supplement 1):ix-xvi
[32] Piercy T, Smither S, Steward J, Eastaugh L, Lever M (2010) The survival of filoviruses in liquids, on solid substrates and in a dynamic aerosol. J Appl Microbiol 109(5):1531-1539
[33] Thieme H (2003) Mathematics in population biology. Mathematical biology series. Princeton University Press, Princeton · Zbl 1054.92042
[34] WHO (2014a) Fact sheet n \[^o\] o103: Ebola virus disease. World Health Organization. http://www.who.int/mediacentre/factsheets/fs103/en/
[35] WHO (2014b) Global alert and response: Ebola virus disease. World Health Organization. http://apps.who.int/ebola/en/ebola-situation-reports
[36] WHO Response Team (2014) Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N Engl J Med 371:1481-1495
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